Sorting. Order in the court! sorting 1

Transcription

1 Sorting Order in the court! sorting 1

2 Importance of sorting Sorting a list of values is a fundamental task of computers - this task is one of the primary reasons why people use computers in the first place Many sorting algorithms have been developed over the history of computer science sorting 2

3 Quadratic sorting algorithms Algorithms with order of magnitude O(N 2 ) are known as quadratic algorithms Such algorithms are particularly sensitive to the size of the data set; sorting times increase geometrically as the size of the data set grows Their quadratic behavior is their chief disadvantage; however, they are easy to comprehend and code, and work reasonably well on relatively small data sets sorting 3

4 Quadratic sorting algorithms We will examine two examples of quadratic sorting algorithms: Selectionsort Insertionsort Each of these algorithms follows the same principle: sort a portion of the array, then progress through the unsorted portion, adding one element at a time to the sorted portion sorting 4

5 Selectionsort Goal of the algorithm is to sort a list of values (for example, integers in an array) from smallest to largest The method employed comes directly from this statement of the problem find smallest value and place at front of array find next-smallest value and place in second position find next-next-smallest and place in third position and so on... sorting 5

6 Selectionsort The mechanics of the algorithm are simple: swap the smallest element with whatever is in the first position, then move to the second position and perform a similar swap, etc. In the process, a sorted subarray grows from the front, while the remaining unsorted subarray shrinks toward the back sorting 6

7 Implementation of Selectionsort public void selectionsort (int data[]) { int mindex, tmp; for (int x = 0; x <= data.length-2; x++) { mindex = x; for (int y = x+1; y <= data.length-1; y++) if (data [y] < data[mindex]) mindex = y; tmp = data[x]; data[x] = data[mindex]; data[mindex] = tmp; } } sorting 7

8 Time Analysis of Selectionsort The driving element of the selectionsort function is the set of for loops, each of which is O(N) Both loops perform the same number of operations no matter what values are contained in the array (even if all are equal, for example) -- so worst-case, average-case and best-case are all O(N 2 ) sorting 8

9 Insertionsort Although based on the same principle as Selectionsort (sorting a portion of the array, adding one element at a time to the sorted portion), Insertionsort takes a slightly different approach Instead of selecting the smallest element from the unsorted side, Insertionsort simply takes the first element and inserts it in place on the sorted side so that the sorted side is always in order sorting 9

10 Insertionsort algorithm Designate first element as sorted Take first element from unsorted side and insert in correct location on sorted side: copy new element shift elements from end of sorted side to the right (as necessary) to make space for new element sorting 10

11 Insertionsort algorithm Correct location for new element found when: front of array is reached or next element to shift is <= new element Continue process until last element has been put into place sorting 11

12 Implementation of Insertionsort public void insertionsort (int [] array) { int x, y, tmp; for (x=1; x<array.length; x++) { tmp = array[x]; for (y=x; y>0 && array[y-1] > tmp; y--) array[y] = array[y-1]; array[y] = tmp; } } sorting 12

13 Time analysis of Insertionsort In the worst case and in the average case, Insertionsort s order of magnitude is O(N 2 ) But in the best case (when the starting array is already sorted), the algorithm is O(N) because the inner loop doesn t go If an array is nearly sorted, Insertionsort s performance is nearly linear sorting 13

14 Recursive sorting algorithms Recursive algorithms are considerably more efficient than quadratic algorithms The downside is they are harder to comprehend and thus harder to code Two examples of recursive algorithms that use a divide and conquer paradigm are Mergesort and Quicksort sorting2 14

15 Divide and Conquer sorting paradigm Divide elements to be sorting into two (nearly) equal groups Sort each of these smaller groups (by recursive calls) Combine the two sorted groups into one large sorted list sorting2 15

16 Using pointer arithmetic to specify subarrays The divide and conquer paradigm requires division of the element list (e.g. an array) into two halves Pointer arithmetic facilitates the splitting task for array with n elements, for any integer x from 0 to n, the expression (data+x) refers to the subarray that begins at data[x] in the subarray, (data+x)[0] is the same as data[x], (data+x)[1] is the same as data[x+1], etc. sorting2 16

17 Mergesort Straightforward implementation of divide and conquer approach Strategy: divide array at or near midpoint sort half-arrays via recursive calls merge the two halves sorting2 17

18 Mergesort Two functions will be implemented: mergesort: simpler of the two; divides the array and performs recursive calls, then calls merge function merge: uses dynamic array to merge the subarrays, then copies result to original array sorting2 18

19 Mergesort in action Original array First split Second split Third split Stopping case is reached for each array when array size is 1; once recursive calls have returned, merge function is called sorting2 19

20 Mergesort method public static void mergesort (int data[], int first, int n) { int n1; int n2; if (n>1) { n1 = n/2; n2 = n-n1; mergesort(data, first, n1); mergesort(data, first+n1, n2); merge(data, first, n1, n2); } } sorting2 20

21 Mergesort method public static void mergesort (int data[], int first, int n) { int n1; int n2; if (n>1) { n1 = n/2; n2 = n-n1; mergesort(data, first, n1); mergesort(data, first+n1, n2); merge(data, first, n1, n2); } } sorting2 21

22 Merge method Uses temporary array (temp) that copies items from data array so that items in temp are sorted Before returning, the method copies the sorted items back into data When merge is called, the two halves of data are already sorted, but not necessarily with respect to each other sorting2 22

23 Merge method in action Two subarrays are assembled into a merged, sorted array with each call to the merge function; as each recursive call returns, a larger array is assembled sorting2 23

24 How merge works As shown in the previous example, the first call to merge deals with several half-arrays of size 1; merge creates a temporary array large enough to hold all elements of both halves, and arranges these elements in order Each subsequent call performs a similar operation on progressively larger, presorted half-arrays sorting2 24

25 How merge works Each instance of merge contains the dynamic array temp, and three counters: one that keeps track of the total number of elements copied to temp, and one each to track the number of elements copied from each halfarray The counters are used as indexes to the three arrays: the first half-array (data), the second half-array (data + n1) and the dynamic array (temp) sorting2 25

26 How merge works Elements are copied one-by-one from the two half-arrays, with the smallest elements from both copied first When all elements of either half-array have been copied, the remaining elements of the other halfarray are copied to the end of temp Because the half-arrays are pre-sorted, these leftover elements will be the largest values in each half-array sorting2 26

27 The copying procedure from merge (pseudocode) while (both half-arrays have more elements to copy) { if (next element from first <= next element from second) { copy element from first half to next spot in temp add 1 to temp and first half counters } else { copy element from second half to next spot in temp add 1 to temp and second half counters } } sorting2 27

28 First refinement // temp counter = c, first counter = c1, second counter = c2 while (both 1st & 2nd half-arrays have more elements to copy) { if (data[c1] <= (data + n1)[c2]) { temp[c] = data[c1]; c++; c1++; } else { temp[c] = (data + n1)[c2]); c++; c2++; } } sorting2 28

29 Final version -- whole algorithm in pseudocode Initialize counters to zero while (more to copy from both sides) { if (data[c1] <= (data+n1)[c2]) temp[c++] = data[c1++]; else temp[c++] = (data+n1)[c2++]; } Copy leftover elements from either 1st or 2nd half-array Copy elements from temp back to data sorting2 29

30 Code for merge public static void merge (int [] data, int first, int n1, int n2) { int [] temp = new int[n1 + n2]; int copied = 0, copied1 = 0, copied2 = 0, i; while ((copied1 < n1) && (copied2 < n2)) { if (data[first + copied1] < data[first + n1 + copied2]) temp[copied++] = data[first + (copied1++)]; else temp[copied++] = data[first + n1 + (copied2++)]; } sorting2 30

31 Code for merge while (copied1 < n1) temp[copied++] = data[first + (copied1++)]; while (copied2 < n2) temp[copied++] = data[first + n1 + (copied2++)]; } for (i=0; i < n1+n2; i++) data[first+i] = temp[i]; sorting2 31

32 Time analysis of mergesort Start with array of N elements At top level, 2 recursive calls are made to sort subarrays of N/2 elements, ending up with one call to merge At the next level, each N/2 subarray is split into two N/4 subarrays, and 2 calls to merge are made At the next level, 4 N/8 subarrays are produces, and 4 calls to merge are made and so on... sorting2 32

33 Time analysis of mergesort The pattern continues until no further subdivisions can be made Total work done by merging is O(N) (for progressively smaller sizes of N at each level) So total cost of mergesort may be presented by the formula: (some constant) * N * (number of levels) sorting2 33

34 Time analysis of mergesort Since the size of the array fragments is halved at each step, the total number of levels is approximately equal to the number of times N can be divided by two Given this, we can refine the formula: (some constant) * N * log 2 N Since the big-o form ignores constants, we can state that mergesort s order of magnitude is O(N log N) in the worst case sorting2 34

35 Comparing O(N 2 ) to O(N log N) N N log N N , , ,144 2,048 22,528 4,194,304 sorting2 35

36 Application of Mergesort Although more efficient than a quadratic algorithm, mergesort is not the best choice for sorting arrays because of the need for a temporary dynamic array in the merge step The algorithm is often used for sorting files With each call to mergesort, the file is split into smaller and smaller pieces; once a piece is small enough to fit into an array, a more efficient sorting algorithm can be applied to it sorting2 36

37 Quicksort Similar to Mergesort in its use of divide and conquer paradigm In Mergesort, the divide part was trivial; the array was simply halved. The complicated part was the merge In Quicksort, the opposite is true; combining the sorted pieces is trivial, but Quicksort uses a sophisticated division algorithm sorting2 37

38 Quicksort Select an element that belongs in the middle of the array (called the pivot), and place it at the midpoint Place all values less than the pivot before the pivot, and all elements greater than the pivot after the pivot sorting2 38

39 Quicksort At this point, the array is not sorted, but it is closer to being sorted -- the pivot is in the correct position, and all other elements are in the correct array segment Because the two segments are placed correctly with respect to the pivot, we know that no element needs to be moved from one to the other sorting2 39

40 Quicksort Since we now have the two segments to sort, can perform recursive calls on these segments Choice of pivot element important to efficiency of algorithm; unfortunately, there is no obvious way to do this For now, we will arbitrarily pick a value to use as pivot sorting2 40

41 Code for Quicksort public static void quicksort (int data[ ], int first, int n) { int pivotindex; // array index of pivot int n1, n2; // number of elements before & after pivot if (n > 1) { pivotindex = partition (data, first, n); n1 = pivotindex - first; n2 = n - n1-1; quicksort (data, first, n1); quicksort (data, pivotindex+1, n2); } } sorting2 41

42 Partition method Moves all values <= pivot toward first half of array Moves all values > pivot toward second half of array Pivot element belongs at boundary of the two resulting array segments sorting2 42

43 Partition method Starting at beginning of array, algorithm skips over elements smaller than pivot until it encounters element larger than pivot; this index is saved (too_big_index) Starting at the end of the array, the algorithm similarly seeks an element on this side that is smaller than pivot -- the index is saved (too_small_index) sorting2 43

44 Partition method Swapping these two elements will place them in the correct array segments The technique of locating and swapping incorrectly placed elements at the the two ends of the array continues until the two segments meet This point is reached when the two indexes (too_big and too_small) cross -- in other words, too_small_index < too_big_index sorting2 44

45 Partition method: pseudocode Initialize values for pivot, indexes Repeat the following until the indexes cross: while (too_big_index < n && data[too_big_index} <= pivot) too_big_index ++ while (data[too_small_index] > pivot) too_small_index -- if (too_big_index < too_small_index) // both segments still have room to grow swap (data[too_big_index], data[too_small_index]) sorting2 45

46 Partition method: pseudocode Finally, move the pivot element to its correct position: pivot_index = too_small_index; swap (data[0], data[pivot_index]); sorting2 46

47 Time analysis of Quicksort In the worst case, Quicksort is quadratic (O(N 2 )) But Quicksort is O(N log N) in both the best and the average cases Thus, Quicksort compares well with Mergesort and has the advantage of not needing extra memory allocated sorting2 47

48 Choosing a good pivot element The worst case of Quicksort comes about if the element chosen for pivot is at either extreme - in other words, the smallest or largest element -- this will occur if the array is already sorted! To decrease the likelihood of this occurring, pick three values at random from the array, and choose the middle of the three as the pivot sorting2 48